Microfluidic image analysis system

ABSTRACT

Technology described herein includes a method that includes obtaining an image of a fluid of a microfluidic analysis system. The microfluidic analysis system includes or receives a container that contains the fluid for measurement of analyte or quality determination. A region of interest (ROI) is identified based on the image. The ROI is a set of pixel values for use in the measurement of the analyte or the quality determination of the fluid, fluidic path, or measuring system. Identifying the ROI includes: determining an alignment of the container of the fluid with the imaging device based on the image, and identifying the ROI based on information about the measurement of the fluid or based on information about non-analyte features of the fluid. An analysis of the image of the fluid is performed using the set of pixel values of the ROI.

TECHNICAL FIELD

This specification generally relates to microfluidic image analysisdevices.

BACKGROUND

A microfluidic analysis device performs analysis of the physical andchemical properties of fluids at a microscale. A microfluidic imageanalysis device often includes a camera that captures an image of thesample fluid. The captured image may be processed to determine variousphysical and chemical properties of the fluid.

SUMMARY

In one aspect, this document describes a method for microfluidicanalysis of fluids. The method includes obtaining an image of a fluid ofa microfluidic analysis system, wherein the microfluidic analysis systemincludes or receives a container that contains the fluid for measurementof analyte or quality determination, and the image is captured using animaging device associated with the microfluidic analysis system;identifying, based on the image, a region of interest (ROI), wherein theROI is a set of pixel values for use in the measurement of the analyteor the quality determination of the fluid, fluidic path, or measuringsystem and wherein identifying the ROI includes: determining analignment of the container of the fluid with the imaging device based onthe image, and identifying the ROI based on information about themeasurement of the fluid or based on information about non-analytefeatures of the fluid; and performing an analysis of the image of thefluid using the set of pixel values of the ROI.

In another aspect, this document describes a system for microfluidicanalysis of fluids. The system includes a microfluidic analysisapparatus that includes or receives a container configured to hold afluid for measurement of analyte or quality determination; an imagingdevice configured to obtain an image of the fluid in the container; andone or more processing devices configured to perform various operations.The operations include identifying, based on the image, a region ofinterest (ROI), wherein the ROI is a set of pixel values for use in themeasurement of the analyte or the quality determination of the fluid,fluidic path, or the microfluidic analysis apparatus, and whereinidentifying the ROI includes: determining an alignment of the containerof the fluid with the imaging device based on the image, and identifyingthe ROI based on information about the measurement of the fluid or basedon information about non-analyte features of the fluid; and performingan analysis of the image of the fluid using the set of pixel values ofthe ROI.

In another aspect, this document describes a non-transitory,computer-readable medium storing one or more instructions executable bya computer system to perform various operations. The operations includeobtaining an image of a fluid of a microfluidic analysis system, whereinthe microfluidic analysis system includes or receives a container thatcontains the fluid for measurement of analyte or quality determination,and the image is captured using an imaging device associated with themicrofluidic analysis system; identifying, based on the image, a regionof interest (ROI), wherein the ROI is a set of pixel values for use inthe measurement of analyte or the quality determination of the fluid,fluidic path, or measuring system and wherein identifying the ROIincludes: determining an alignment of the container of the fluid withthe imaging device based on the image, and identifying the ROI based oninformation about the measurement of the fluid or based on informationabout non-analyte features of the fluid; and performing an analysis ofthe image of the fluid using the set of pixel values of the ROI.

Implementations of the above aspects can include one or more of thefollowing features. The fluid is a whole blood sample, and the imagerepresents the whole blood sample with blood plasma separated from redblood cells. Identifying the ROI includes identifying, in the image, aportion representing the blood plasma, wherein identifying the portionrepresenting the blood plasma includes: detecting a plurality ofreference features associated with the container of the fluid;identifying, based on the reference features, a candidate region for theROI; and performing clustering-based thresholding of pixel values withinthe candidate region to identify the portion representing the bloodplasma. Alternative implementations may use neural networks for ROIdetection and image segmentation instead of the clustering-basedthresholding. The measurement of the analyte includes a parameterindicative of hemolysis (hemoglobin) in a portion representing bloodplasma. The measurement of the analyte includes a parameter indicativeof lipemia (or lipids) in a portion representing blood plasma. Themeasurement of the analyte includes a parameter indicative of Icterus(or bilirubin) in a portion representing blood plasma. The method or theoperations can further include determining that the ROI excludes aportion that represents lipid in blood plasma; and identifying anupdated ROI such that the updated ROI includes a bounding box thatincludes the portion that represents the lipid. The qualitydetermination of the fluid, the fluidic path, or the measuring systemincludes: determining quality of an assay, determining quality of asample, and determining integrity of the fluidic path or the measuringsystem impacting the ROI. The quality determination of the fluid, thefluidic path, or the measuring system includes: determining that the ROIincludes a portion that represents an air bubble in the fluid; andidentifying an updated ROI such that the updated ROI excludes theportion that represents the air bubble. The method or the operations canfurther include detecting an amount of tilt in the image of the fluid,the tilt resulting from the alignment of the container of the fluid withthe imaging device; and generating, based on the amount of the tilt, arotation-corrected image of the fluid, wherein the ROI is identified inthe rotation-corrected image. Performing the analysis of the image ofthe fluid includes: generating an image focus score associated with theimage; determining that the image focus score is lower than apredetermined threshold; and discarding the image of the fluidresponsive to determining that the image focus score is lower than thepredetermined threshold. Performing the analysis of the image of thefluid includes: identifying, in the image, a portion representing atransparent portion of the container; and using brightness of theportion representing the transparent portion of the container as areference point to evaluate brightness of other portions of the image.The method or the operations can further include monitoring one or moreoptical characteristics of the fluid at predetermined intervals. Themethod or the operations can further include identifying a targetoptical interference pattern in the image; and generating an alert inresponse to identifying the target optical interference pattern in theimage. The blood plasma is separated from the red blood cells within themicrofluidic analysis apparatus using 2-150 uL of the whole bloodsample.

Particular implementations of the subject matter described in thisdisclosure can be implemented to realize one or more of the followingadvantages. The implementations of the present disclosure can performmicrofluidic analysis by implementing machine vision processes that areadaptive and automatic. The described microfluidic analysis system canidentify a region of interest (ROI) in images of fluids even when thecharacteristics of the ROI vary significantly, e.g., when the ROI doesnot have fixed shape, fixed image intensity, or fixed location in thecorresponding containers of the fluids, and so on. As such, thedisclosed technology can account for any alignment-variation between animaging system and the unit/entity (e.g., a container) that the imagingsystem captures. The disclosed technology can also account forinhomogeneity of a sample (e.g., whole blood) by automatically includingor excluding elements such as air bubbles, lipids etc., to identify anaccurate ROI suited for a specific application. In certain microfluidicanalysis systems—e.g., in whole blood analysis systems, where accurateidentification of the region of interest governs the accuracy of theresults—the adaptive and automatic ROI identification can improve theunderlying technology in various ways. For example, implementations ofthe present disclosure can automatically identify ROIs while improvingprocessing time as well as accuracy attributable to potential humanerrors.

In some implementations, by monitoring one or more opticalcharacteristics of a fluid at predetermined intervals or at everyinstance, the automated and adaptive processes described herein canfacilitate a substantially continuous quality control of various aspects(e.g., the quality of an assay, the quality of a sample, or theintegrity of the fluidic path or the measuring system impacting the ROI)of the underlying system. For example, the microfluidic analysis systemcan identify a target optical interference pattern in the image, e.g.,one that is representative of an air bubble or poor ROI region in animage of a blood sample, and can send an alert to a user of the imagingdevice accordingly, and/or discard samples that do not meet targetquality criteria.

It is appreciated that methods and systems in accordance with thepresent disclosure can include any combination of the aspects andfeatures described herein. That is, methods and systems in accordancewith the present disclosure are not limited to the combinations ofaspects and features specifically described herein, but also may includeany combination of the aspects and features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an example device configured to implementtechnology described herein.

FIG. 2 is a flowchart of an example process for performing microfluidicanalysis in accordance with technology described herein.

FIG. 3A shows an example of an ROI identified in an image of a wholeblood sample.

FIG. 3B shows an example of clustering-based thresholding.

FIG. 3C shows an example of poor plasma separation in an image of awhole blood sample.

FIG. 4A shows an example of an image of a whole blood sample thatincludes lipids.

FIG. 4B shows an example of an initial ROI that excludes the lipids.

FIG. 4C shows an example of an updated ROI that includes the lipids.

FIG. 5A shows an example of an initial ROI that includes an air bubble.

FIG. 5B shows an example of an optical density (OD) histogram of theinitial ROI.

FIG. 5C shows an example of an updated ROI that excludes the air bubble.

FIG. 5D shows an example of an optical density (OD) histogram of theupdated ROI.

FIG. 5E shows an example of an elongated air bubble in an image of awhole blood sample.

FIG. 6A shows an example of an image of a flow-cell that is tilted.

FIG. 6B shows a thresholded image of FIG. 6A.

FIG. 6C shows a rotation-corrected image of FIG. 6A.

FIG. 7 is a schematic diagram of an example process for generating animage focus score.

FIG. 8A shows an example of a portion of a reference image, the portionrepresenting a transparent region.

FIG. 8B shows an example of a portion of an image of a whole bloodsample, the portion representing a transparent region.

FIG. 9 is a flowchart of an example process for performing blood sampleanalysis in accordance with technology described herein.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

In various fluidic and microfluidic applications, accurateidentification of a region of interest (ROI) can be very important. Forexample, hemolysis detection/measurement entails separating whole bloodinto red blood cells and plasma, and then measuring an amount ofhemoglobin in the plasma. Therefore, for accurate image-based hemolysisdetection and measurement, it is important to accurately identify an ROIin the image such that the ROI includes only the plasma region andexcludes the red blood cells as well as other artifacts such as airbubbles. A microfluidic image analysis device can include, or canreceive, a container (e.g., a cartridge, a vial, a cuvette, or others)that contains a sample fluid (e.g., whole blood sample that is thenseparated into red blood cells and plasma) for analysis. An imagecapture device such as a camera can be used to capture an image of thesample fluid such that the captured image can be processed by machinevision processes to determine one or more physical and chemicalproperties of the fluid. Typical microfluidic analysis systems oftenidentify an ROI based on predetermined assumptions about the ROI such asa predetermined shape and/or a predetermined location within thecontainer of the fluid. In practice though, such assumptions can lead topotential inaccuracies. For example, the relative location/orientationof a container with respect to the image capture device can vary fromone instance (e.g., measurement or test) to another, rendering anyassumptions based on a fixed size or location of the ROI susceptible toinaccuracies. Also, in some cases, there may be impurities (e.g., airbubbles) present within the ROI that need to be accounted for. Thetechnology described in this document provides for adaptive, automaticsystems and processes that identify ROIs in images without predeterminedassumptions with respect to the shape, location, and/or orientation ofthe ROIs. Specifically, in some implementations, the disclosedtechnology uses particular features of the container (e.g., edges) asreference features to correct for any orientation/location variationsand facilitates determination of sample-specific, arbitrary-shaped ROIswhile potentially accounting for impurities, particles within the ROIs.

FIG. 1 is a diagram of an example sample analysis device 100 configuredto implement technology described herein. The sample analysis device 100includes an optical module 101 and a microfluidic analysis system 112 incommunication with the optical module 101. The optical module 101 isconfigured to obtain an image of a fluid 106 in a container 102. In someimplementations, the fluid 106 can be configured to flow through thecontainer 102 or can be contained within the container 102. In someimplementations, the container 102 is a cartridge within which a wholeblood sample may be separated into red blood cells and plasma forhemolysis detection/measurement.

In some implementations, the container 102 is included as a part of theoptical module 101. For example, the container 102 can be a microchannelfluid container that is a built-in component or an inserted or add-oncomponent of the optical module 101. In some implementations, theoptical module 101 is configured to receive the container 102, e.g., asa disposable cartridge. In some implementations, the container 102 canbe a microfluidic flow-cell.

In some implementations, the sample analysis device 100 can include anacoustic transducer. The acoustic transducer can be, for example, apiezo-electric transducer that is arranged in close proximity to thecontainer 102 such that acoustic energy can be applied by the acoustictransducer to the fluid 106 in the container 102. For example, theacoustic transducer can be activated by an electrical signal to generateacoustic energy that causes separation of red blood cells from plasma ina whole blood sample. In some implementations, the container 102 is aflow cell, and the piezo-electric transducer is bonded to or part of theflow-cell. In such cases, the acoustic energy transmitted to the fluidwithin the flow-cell can vary depending on the properties of the bond(e.g. bond strength, thickness, etc.).

The optical module 101 can also include a light source 107. The lightsource 107 is arranged to transmit light waves through the container 102to the fluid 106 that is flowing through or contained (e.g., stationarywithout flowing) in the container 102. For example, the light source 107can be configured to transmit the light waves through a plasma portionof the blood sample that is separated from red blood cells in a wholeblood sample. In some implementations, the light source 107 can includea multi-color light emitting diode, e.g., a 2-color LED emitting red andyellow lights. The optical module 101 can include a camera 104, oranother optical sensor configured to generate an image 108 of the fluid106. In some implementations, the camera 104 can include an imaging lensand an aperture. The image 108 can be a grayscale image or a colorimage, which is then analyzed by the microfluidic analysis system 112.

The specific example of the image 108 shown in FIG. 1 pertains tohemolysis detection/measurement where a blood sample is separated intoplasma and red blood cells. In some implementations, plasma separationfrom the red blood cells can be performed by centrifuging the wholeblood sample. Plasma separation from the red blood cells using acousticapplications is also described in U.S. Ser. No. 15/791,734, the entirecontent of which is incorporated herein by reference. The scope of thedisclosed technology however is not limited to hemolysisdetection/measurement only, and can be extended to other applications(e.g., colorimetric measurements of analytes in blood). The specificarrangements of the device components as described above and shown inFIG. 1 do not affect implementations of the methods and systemsdescribed in this disclosure. As described below, the technology usesinformation from an image of a sample fluid and selected information ofthe device arrangement to automatically determine the ROI that isspecific to the image.

In the particular example of hemolysis detection shown in FIG. 1 , theimage 108 includes portions that correspond to the container102—including transparent areas 115, e.g., glass or other transparentmaterials, and flow-cell edges 109—and portions that correspond to thewhole blood sample, which are located between the flow-cell edges 109.The image 108 shows that the plasma 114 has been separated from the redblood cells 116. This separation allows clear plasma to be interrogatedoptically to determine a hemoglobin level in the plasma 114. As such, inthis particular example of hemolysis detection, the region 110corresponding to the plasma 114 (shown using the dashed box) is the ROI.

The characteristics of an ROI, e.g., the plasma ROI 110, can varysignificantly from one instance to another for various reasons. Forexample, in implementations, where the container 102 is a flow-cell andthe piezo-electric transducer is bonded to the flow-cell, imperfectionsin the bonding process can introduce bond variations from one flow-cellto another, causing the ROI 110 to potentially assume varying shapes andpositions during the process of separating the red blood cells 116 fromplasma 114. Another source of variation affecting the ROI 110 can besome unintentional relative tilt between the container 102 and thecamera 104 introduced during inserting/assembling the container 102(e.g., the flow-cell). In some implementations, ROI image intensityvariation can also arise due to subtle differences in illumination andcamera sensitivity.

In some implementations, sample-to-sample variation can also causevariations in the ROI. For example, concentration of particles in thefluid 106 can determine how much acoustic energy is needed to create theparticle-free ROI 110 within the field of view of the camera.Insufficient acoustic energy delivered to the sample containing highparticle concentration can lead to an area that is too small forsubsequent analytical optical absorbance measurement to be performed.Another major sample-dependent source of image variation (and byextension, a variation in the ROI) is the concentration of lightscattering particles such as lipids. The presence of such lightscattering particles can make the images appear darker than thatexpected in accordance with the light absorbing properties of theparticles of interest. In some implementations, presence of air or othergas bubbles within ROIs can be sources of variations in the ROI.

The microfluidic analysis system 112 can be configured to account forthe different variations in the ROI and identify sample-specific ROIs.The microfluidic analysis system 112 can be implemented using one ormore processing devices. The one or more processing devices can belocated at the same location as the optical module 101, or reside on oneor more servers located at a remote location with respect to the opticalmodule 101. The microfluidic analysis system 112 can be configured toidentify the ROI 110 in the image 108 captured by the optical module101. The identified ROI is a set of pixel values that are then used inthe measurement of an analyte or in determining the quality of thefluid, fluidic path, or other portions of the sample analysis device100.

The microfluidic analysis system 112 can be configured to perform ananalysis of the image 108 of the fluid 106 using a set of pixel valuesof the ROI. The analysis can include, for example, measurement of one ormore analytes in the fluid, or determination of the quality of thefluid, fluidic path, or measuring system. For example, the system 112can be configured to perform measurements (e.g., hemolysis detectionmeasurement) on blood plasma separated from the blood cells in a wholeblood sample. In some implementations, the system 112 can be configuredto evaluate sample quality, for example, by determining/detecting thepresence of a clot in the sample, and/or identifying non-analytefeatures that can potentially interfere with accuracy of measurements,e.g., a tilted image, an out-of-focus image, and so on.

FIG. 2 is a flowchart of an example process 200 performing microfluidicanalysis in accordance with technology described herein. In someimplementations, at least a portion of the process 200 may be executedby one or more processing devices associated with the microfluidicanalysis system 112 described with reference to FIG. 1 . In someimplementations, at least a portion of the process 200 may be executedby the optical module 101. In some implementations, at least a portionof the process 200 may be executed at one or more servers (such asservers or computing devices in a distributed computing system), and/orone or more mobile devices (such as smartphones) in communication withthe sample analysis device 100 described with reference to FIG. 1 .

Operations of the process 200 include obtaining an image of a fluid of amicrofluidic analysis system (202). The microfluidic analysis systemincludes or receives a container that contains the fluid for measurementof analyte or quality determination. The image is captured using animaging device associated with the microfluidic analysis system. In someimplementations, the container can be substantially similar to thecontainer 102 described above with reference to FIG. 1 . For example, asshown in FIG. 1 , the fluid within the container can be a whole bloodsample, and the image can represent the whole blood sample with bloodplasma separated from red blood cells. The blood plasma can be separatedfrom the red blood cells locally on the sample analysis device 100depicted in FIG. 1 . In some implementations, the sample size of thewhole blood can be in the range 2-150 uL.

Operations of the process 200 also include identifying, based on theimage, an ROI (204). The ROI is a set of pixel values for use in themeasurement of an analyte or the quality determination of the fluid,fluidic path, or measuring system. Because the ROI may not have fixedshape, fixed image intensity, or fixed location, correctly identifyingthe ROI can potentially affect the accuracy of the measurement of theanalyte in the ROI or performing quality determination of the fluid,fluidic path, or the measuring system.

In some implementations, identifying the ROI can include determining analignment of the container of the fluid with the imaging device based onthe image. For example, an alignment can be determined by firstcalculating a flow-cell tilt and then finding the location of theflow-cell edges. For example, an alignment/orientation of the containerwith respect to the image capture device (e.g., the camera 104 in FIG. 1) can be dynamically measured (e.g., each time a new container isinserted or received within the optical module 101), and anymisalignment/tilt can be accounted for prior to identifying the ROI(such as correcting for any misalignment during device shipment or fromvibration during device use)

In some implementations, identifying the ROI can include identifying theROI based on information about the measurement of the fluid. Forexample, an ROI of a high lipid sample can be in the shape of a boundingbox. As another example, for reference fluid, an ROI can be in arectangular shape with specified dimensions relative to the innerflow-cell wall. As another example, an ROI for a blood sample can bedynamically calculated.

In some implementations, identifying the ROI can be based on informationabout non-analyte features represented in the image. For example, theROI can be identified based on edges of a container or other fiducialmarkers represented in the image.

Referring to FIG. 1 , in some implementations, identifying the ROI 110can include identifying, in the image 108, a portion 110 representingthe blood plasma 114. In some implementations, identifying the portionrepresenting the blood plasma can include: detecting a plurality ofreference points associated with the container of the fluid,identifying, based on the reference points, a candidate region for theROI, and performing clustering-based thresholding of pixel values withinthe candidate region to identify the portion representing the bloodplasma. This is illustrated with an example in FIG. 3A. Specifically,FIG. 3A shows an example of an ROI identified in an image of a wholeblood sample. In this example, a plurality of reference featuresassociated with the container of the fluid—e.g., the edges 304, 306,308, and 310 of the flow-cell—are first identified. Various edgedetection algorithms—such as the Canny Edge Detection algorithm (Canny,John. “A computational approach to edge detection.” IEEE Transactions onpattern analysis and machine intelligence 6 (1986): 679-698.)—can beused for this purpose. Next, the system can be configured to determinewhich edges correspond to the top inner edge 306 of the flow-cell andthe bottom inner edge 308 of the flow-cell. For example, the system caniterate through the candidate edges starting from top to bottom, andsequentially find the top outer edge 304, the top inner edge 306, thebottom inner edge 308, and the bottom outer edge 310.

Once the reference features are identified, the system can be configuredto identify, based on the reference features, a candidate region for theROI. For example, after detecting the inner edges 306 and 308 of theflow-cell, the system can identify a candidate region for the ROI as aregion between the top inner edge 306 and the bottom inner edge 308. Inthe example of FIG. 3A, the candidate region can include both the plasmaregion and the red blood cells region.

In some implementations, identifying the actual ROI (e.g., the bloodplasma region 312 in FIG. 3A) within the candidate ROI can includeperforming clustering-based thresholding of pixel values within thecandidate region. An example of such clustering is shown in FIG. 3B,where multiple clusters of pixel intensities are generated, and one ormore threshold values are then used to determine the actual ROI. Variousclustering algorithms and tools may be used for this purpose. FIG. 3Bshows an example of clustering-based thresholding described in thefollowing publication—Otsu, Nobuyuki. “A threshold selection method fromgray-level histograms.” IEEE transactions on systems, man, andcybernetics 9.1 (1979): 62-66—the entire content of which isincorporated herein by reference. Specifically, FIG. 3B shows an exampleof the distribution of the pixel intensities within the candidateregion, i.e., the region of the image in FIG. 3A between the top inneredge 306 and the bottom inner edge 308. In this example, an assumptionis made that the candidate region for the ROI has two classes of pixels,i.e., red blood cells and plasma, and due to differential lightabsorbing properties of red blood cells and plasma, the plasma pixelsare brighter than the red blood cell pixels. Under these assumptions,multiple clusters of pixels may be identified in the histogram accordingto their pixel intensities, and an appropriate threshold may be used toseparate the plasma pixels from the red blood cells pixels. While theexample of FIG. 3B shows two peaks or clusters, in other applications,multiple clusters may be present, and as such, multiple ROIs may beidentified. In some implementations, alternatively, one or more neuralnetworks can be used for ROI detection and image segmentation instead ofor in addition to the clustering-based thresholding described herein.

In some implementations, the plasma in an image of a whole blood samplemay not be well separated from the red blood cells. Such samples may notbe suitable for a particular application such as hemolysisdetection/measurement. The disclosed technology can be used in suchcases to automatically detect and discard such unsuitable samples, e.g.,alert a user without further measurement, from further analysis. Anexample of such a sample image is shown in FIG. 3C, which shows anexample of poor plasma separation in an image of a whole blood sample.Specifically, while three plasma ROIs 314 are identified in the image,the small size of the identified ROIs make them prone to inaccuratemeasurements and hence unsuitable for further analysis. In someimplementations, a determination can be made that the size of an ROI(e.g., the ROIs 314) is smaller than a threshold value, and accordingly,the corresponding sample/image can be marked as unsuitable for furtheranalysis and measurements. The threshold value can be a parameter of themicrofluidic analysis system that can be configured and modified.

Operations of the process 200 also include performing an analysis of theimage of the fluid using the set of pixel values of the ROI (206). Insome implementations, performing the analysis of the image of the fluidcan include performing measurement of the analyte. In someimplementations, the measurement of the analyte can include a parameterindicative of hemolysis (e.g., hemoglobin) in a portion representingblood plasma. For example, the system can be configured to apply anoptical density (OD) algorithm or a concentration algorithm to generatea histogram from the pixel values of the ROI, e.g., the ROIcorresponding to the plasma region. The system can be further configuredto identify the peak of the generated histogram, and use the peak of thehistogram to calculate a hemoglobin value. The hemoglobin value canindicate the presence/degree of hemolysis in the blood plasma.

In some cases, identifying an ROI based purely on pixel sample intensitycan be challenging, particularly in the presence of sample-dependentsource of image variations such as the concentration of light scatteringparticles such as lipids. The presence of such light scatteringparticles can make images appear darker than what might be expected inaccordance with the light absorbing properties of the particles ofinterest, and therefore interfere with accuracy of measurements. In someimplementations, the operations of the process 200 can further includedetermining that the ROI excludes a portion that represents lipid inblood plasma, and identifying an updated ROI such that the updated ROIis a bounding box that includes the portion that represents the lipid.FIG. 4A shows an example of an image of a whole blood sample thatincludes lipids. Lipid particles can cluster near the center of the flowchannel when acoustic energy is imparted to the fluidic channel. Thistype of lipid clustering can result in darker pixels 402 near the centerof the flow channel.

FIG. 4B shows an example of an initial ROI that excludes the lipids.When a clustering-based thresholding of pixel values is performed withinthe candidate region to identify the ROI, the dark pixels 404 thatcorrespond to the lipids may be excluded from the ROI because thosepixels have pixel values that are below the threshold value identifiedby the clustering-based thresholding algorithm. The threshold value canbe a parameter of the microfluidic analysis system that can beconfigured (e.g., via programming into a memory such as EEPROM) andmodified as needed. Consequently, the measurements performed in theidentified ROI 406 may not be accurate. In some implementations,additional processing may be performed based on the identified ROIs suchthat the lipid pixels are included within the automatically identifiedROI. For example, the system can estimate the amount of lipid pixelsdetected in the initial ROI identified by the clustering-based method.The system can determine whether the estimated amount of lipid exceeds apre-defined lipid concentration threshold. If the system determines thatthe estimated amount of the lipid exceeds the pre-defined lipidconcentration threshold, a bounding box 408 can be generated around theinitial ROI to include the lipid pixels. The bounding box 408 caninclude both the ROI 406 and the dark pixels 404 that correspond to thelipids. Therefore, the system can generate an updated ROI thatcorresponds to the region of the bounding box 408 such that the updatedROI includes the lipids.

In some implementations, the quality determination of the fluid, thefluidic path, or the measuring system can include determining that theROI includes a portion that represents an air bubble in the fluid, andidentifying an updated ROI such that the updated ROI excludes theportion that represents the air bubble because the air bubble may affectthe analytical quality of the measurement in the ROI. For example, thepixels for an air bubble are not representative of the hemolysis levelin a blood sample, and hence including the air bubble pixels in themeasurement can introduce an analytical error. This is shown withexamples in FIG. 5A-5E. Specifically, FIG. 5A shows an example of aninitial ROI that includes an air bubble. The initial ROI includes afirst portion 502 that represents the plasma in the blood sample and asecond portion 504 that represents a large air bubble in the bloodsample. In this example, the system can calculate an optical density(OD) values for each pixel in the initial ROI, and can plot a histogramof the OD values of the pixels in the initial ROI. The histogram has twopeaks 510 and 512, as shown in FIG. 5B. The first peak 510 correspondsto the portion 504 that represents the air bubble in the blood sample, asecond peak 512 corresponds to the portion 502 that represents theplasma in the blood sample. Because the air bubble 506 is relativelylarge, the peak 510 can be higher than the peak 512. Therefore, insteadof detecting the peak 512, the system may detect the peak 510 as thehighest peak of the histogram, and may calculate an inaccuratehemoglobin value based on the peak 510.

In order to avoid this inaccuracy, a determination may be made that theinitial ROI includes an air bubble—by calculating a ROI quality metricof the initial ROI in FIG. 5A. Because plasma ROIs are typicallyexpected to have a particular shape, (e.g., a shape of the flow channel,such as a rectangular shape or a round shape), an index score can becalculated to represent the level of deviation from the expectedparticular shape. For example, if the plasma ROI is expected to have arectangular shape, the degree of non-rectangularity can be calculated todetermine a likelihood that the initial ROI includes one or more airbubbles.

For example, referring again to FIG. 5A, the system can be configured togenerate a bounding box 506 that includes the initial ROI, i.e., theplasma portion 502 and the air bubble portion 504. The area of theinitial ROI, i.e., a sum of the plasma portion 502 and the air bubbleportion 504, can be calculated, for example, by setting the pixelsoutside the portions 502 and 504 to zero, and calculating the number ofnon-zero pixels within the within the bounding box 506. In someimplementations, the non-rectangularity may be calculated as:

Non-rectangularity=1−(Area of the initial ROI/Area of the boundingbox).  (1)

The non-rectangularity is therefore a value between 0 and 1. A smallernon-rectangularity value can indicate that an ROI is more likely to havea rectangular shape. A larger non-rectangularity value can indicate thatan ROI is less likely to have a rectangular shape and more likely tohave an air bubble. For example, the non-rectangularity value of theinitial ROI in FIG. 5A (the portions of 502 and 504) can be 0.7,indicating that the initial ROI is likely to have an air bubble. Othermorphological analyses may also be performed to determine whether aparticular portion of the determined ROI is likely to be an air bubble(or another gas bubble). For example, a roundness index can be computedfor each blob as a ratio of (i) the number of pixels in the blob to (ii)number of pixels in (or an area of) a circle enclosing the blob. In someimplementations, if the roundness index for a particular blob is higherthan a threshold value (e.g., higher than 0.5), the particular blob isdetermined as a likely air bubble and may be excluded from beingconsidered as a part of the ROI.

In general, once one or more air bubbles are detected, an updated ROIcan be generated such that the updated ROI excludes the portion thatrepresents the one or more air bubbles. FIG. 5C shows an example of anupdated ROI that excludes the air bubble. In this example, the portion504 of the initial ROI (FIG. 5A) is determined to correspond to an airbubble 507, and accordingly, an updated ROI only includes the portion502 that represents the plasma in the blood sample, and excludes the airbubble portion 504.

The updated ROI can then be used for subsequent processing. For example,when the identified ROI is used for hemolysis detection/measurement, anOD algorithm can be applied to generate a histogram as shown in FIG. 5D.Because the portion 504 corresponding to the air bubble is removed fromthe updated ROI, the histogram in FIG. 5D only has one peak 512corresponding to the plasma portion 502. Further calculations based onthis are therefore more accurate as compared to that based on thebimodal histogram of FIG. 5B.

FIG. 5E shows an example of an elongated air bubble in an image of awhole blood sample. In this example, the ROI in the whole blood sampledoes not have sufficient plasma area. The system can determine that theROI does not have sufficient plasma area through a set of ROI qualitychecks. The system can suppress the sample reporting and can alarm anend-user. In some implementations, the set of ROI quality checks caninclude one or more of the following: 1) abnormal blob height, 2) lackof plasma near flow-cell edges, and 3) elevated blob roundness. Based onthe set of ROI quality checks, the elongated bubble pixels can beremoved from the ROI and would not be included in a subsequent analysis.In some cases, sufficient plasma pixels can remain in or near anotherblob(s), and the sample measurement can still be made and reported tothe end-user.

In some implementations, correctly identifying an ROI also includesaccounting for any unintentional relative tilt between an image capturedevice (e.g., the camera 104 in FIG. 1 ) and the container within whichthe fluid to be imaged is disposed (e.g., the container 102 in FIG. 1 ).Such tilts or orientation variations can be introduced, for example,during inserting/assembling the fluid container within the testapparatus. For example, when the container is a disposable cartridge,orientation variations or tilts can occur when one cartridge is replacedwith another. In some implementations, the technology described hereinfacilitates detecting an amount of tilt in the image of the fluid,generating, based on the amount of the tilt, a rotation-corrected imageof the fluid, and identifying the ROI in the rotation-corrected image.

FIG. 6A shows an example of an image of a fluid that is tilted. Theimage 602 is a raw image captured by the camera. The image 602 showsmisalignment between a camera and a flow-cell. In some implementations,an amount of tilt in the image 602 can be estimated by generating athresholded image, e.g., by Otsu thresholding, and rotation-correctingthe thresholded image. Other implementations may include using anArtificial Neural Network (NN) or a Hough Transform to detect theflow-cell edges and to estimate the tilt angle. FIG. 6B shows athresholded image generated from the example of FIG. 6A using Otsuthresholding. The tilt angle can be estimated, for example, bycalculating an angle of one of the detected edges, e.g., the bottomouter edge 604, with respect to the horizontal boundary of the image.Based on the estimated amount of tilt, a rotation-corrected image of thefluid can be generated. FIG. 6C shows a rotation-corrected image of FIG.6A. The ROI identification can then be performed on therotation-corrected image.

In some implementations, performing sample quality evaluation caninclude generating an image focus score associated with the image,determining that the image focus score is lower than a predeterminedthreshold, and discarding the image of the fluid in response todetermining that the image focus score is lower than the predeterminedthreshold. Intrinsic and fixed image features such as sharp edges can beused as a target for image focus evaluation, providing an advantage overthe traditional approach where an external target is introduced toevaluate the image focus. FIG. 7 shows an example of generating an imagefocus score by measuring contrasts of pixels near an edge in the image.FIG. 7 includes a region 702 that corresponds to a zoomed in view of thetop outer edge of the flow-cell. The box #1 (706) has 30×300 pixels, andis centered on the exterior flow-channel edge. The box #2 (704) has30×300 pixels, and is a box that is generated by shifting the box 706 byone pixel downward. In some implementations, an image focus score can begenerated by performing the following steps: (1) normalizing every pixelby an overall image brightness; (2) summing the normalized pixels acrossthe columns in Box 706, wherein the result will be a first 30×1 array ofvalues; (3) summing the normalized the pixels across the columns in Box704, wherein the result will be a second 30×1 array of values; (4)estimating a two point derivative by subtracting the second array fromthe first array; (5) determining the maximum absolute value of the twopoint derivative, wherein the maximum absolute value is an estimatedfocus score of the top outer edge; (6) repeating steps (2)-(5) for thebottom outer edge of the flow-cell; and (7) generating an average focusscore based on the estimated focus scores for the top outer edge and thebottom outer edge. Other types of image focus estimation metrics, suchas a Brenner function score, may also be used. In some implementationsimages with focus scores less than a threshold may be discarded, forexample, because measurements performed on an out-of-focus image may becompromised. In some implementations, the system can generate an alertin response to discarding an image.

In some implementations, determining sample-specific ROIs can includeaccounting for variations due to illumination from one sample toanother. For example, power fluctuations in the light source (e.g., anLED) can introduce variations in the brightness of corresponding imagescaptured by the camera. Specifically, when the camera captures twoimages at two different times, the images may show different brightnessdue to the fluctuation of the LED power. In some implementations,performing the analysis of the image of the fluid can includeidentifying, in the image, a reference portion (e.g., a transparent,e.g., glass, portion of the container), and using brightness of thereference portion to normalize/evaluate brightness of other portions ofthe image.

For example, when executing an OD algorithm, both a reference imagewithout a blood sample and an image with a blood sample can be capturedand compared with one another. Referring to the example in FIG. 8A, thesystem can identify, in the reference image 806, a portion representinga transparent, e.g., glass, portion 802 of the container. The system canalso identify, in the blood sample image 808 (FIG. 8B), a correspondingportion 804 representing a transparent portion of the container. Thebrightness of the transparent portion can be used as a reference pointto evaluate the brightness of other portions of the image. For example,the system can compensate for the brightness variation between thereference image 806 and the blood sample image 808 by comparing thebrightness of the corresponding transparent portions 802 and 804. Inexecuting an OD algorithm, an optical density can be generated, forexample, by normalizing the pixel value in the reference image and bloodsample image with the pixel value in their respective transparentportions. For example, the OD calculation can be the following:

$\begin{matrix}{{OD} = {\log{\left( \frac{{Ref}/I_{0}}{{Sample}/I_{1}} \right).}}} & (2)\end{matrix}$

Here, Ref is a pixel value in the reference image 806. Blood is a pixelvalue of a corresponding pixel in the blood sample image 808. I₀ is thebrightness of the transparent region 802 in the reference image 806, andI₁ is the brightness of the transparent region 804 of the blood sampleimage 808.

In some implementations, one or more optical characteristics of theon-board calibration solutions may be monitored at predeterminedintervals to ensure continued quality checks, and to potentiallycalibrate analytical performance. For example, the system can monitorfor residual blood clots, lipids, air bubbles, image focus, and so on,at predetermined intervals. In some implementations, this can includeidentifying a target optical interference pattern in the image, andgenerating an alert in response to identifying the target opticalinterference pattern. Examples of the target optical interferencepatterns can include interference patterns representing air bubbles,debris in the field-of-view, and carryover material from the sample,etc.

Referring back to FIG. 3C, the image shows an example of poor plasmaseparation in a whole blood sample detected through a quality monitoringprocess described above. Similarly, FIG. 5E shows an example of anelongated air bubble in an image of a whole blood sample. Upon detectingsuch poor plasma separation, elongated air bubble, or other artifactsthat potentially compromises the accuracy of the results, the system canbe configured to generate an alert and/or exclude the image from furtherprocessing. After receiving the alert, a user may manually review theimage, or decide to discard the image.

In some implementations, other tests can be performed on the identifiedROI in the fluid sample, e.g., colorimetric measurements of analytes inblood. The system can determine the ROI in a similar way as the systemdetermines an ROI for a hemolysis test. In some implementations, thesystem can determine the ROI based on the specific test that is to beperformed. For example, the system can determine the ROI based on areasof the image that changes color in response to one or more of thefollowing: an analyte present in the sample, antibody/antigen bindingreaction, and staining targeting specific parts of the cell.

The system can be configured to allow a user to choose or automaticallychoose a particular test among different tests such that parameters fora particular test (e.g., preprogrammed parameters) can be determinedbased on the chosen test. In some implementations, the system can allowa user to enter desired parameters for a test (e.g., desired sensitivityand/or specificity levels in performing an analysis).

FIG. 9 is a flowchart of an example process 900 for performing bloodsample analysis in accordance with technology described herein. Theexample process 900 includes an example imaging process (e.g., steps902, 904, 906, 908, 910, and 912). The example process 900 also includesan example quality monitoring process (e.g., steps 904, 906, 914, 916,918, 920). In some implementations, at least a portion of the process900 may be executed by one or more processing devices associated withthe microfluidic analysis system 112 described with reference to FIG. 1. In some implementations, at least a portion of the process 900 may beexecuted by the optical module 101. In some implementations, at least aportion of the process 900 may be executed at one or more servers (suchas servers or computing devices in a distributed computing system) incommunication with the sample analysis device 100 described withreference to FIG. 1 .

Operations of the process 900 include obtaining a raw image captured bya sample analysis device (902). The raw image can be an image of a wholeblood sample with blood plasma separated from red blood cells. Forexample, the optical module 101 in FIG. 1 can capture the raw image. Insome implementations, the system can obtain multiple groups of images asinput to the process 900. The multiple groups of images can include oneor more frames of dark images, one or more frames of reference images,and one or more frames of sample images. Dark images are captured toaccount for optical dark signal from the electronics. Reference imagesare captured to compensate for light transmission without the samplefluid. Each image frame can include one or more color images (e.g., ared image and a yellow image). In some implementations, the system cangenerate an image that is ready for subsequent analysis from themultiple groups of images. The system can perform image subtraction tocompensate for contributions from electronic and optical noise. Forexample, the system can subtract the dark images from the sample images.As another example, the system can subtract the dark yellow image fromthe sample yellow image. In some implementations, the system can smooththe raw image (or an image resulting from the subtraction process) byapplying a smoothing filter such as a Gaussian blur filter.

Operations of the process 900 also include performing tilt correction onthe raw image and generating a tilt corrected image (904). For example,the system can perform the tilt correction in accordance with thetechniques described in connection with FIGS. 6A-6C. In someimplementations, the system can import a single frame (e.g., the firstframe) of a sequence of images and perform the tilt correction based onthe single frame of the sequence of images. For example, the system canimport the first frame of the blood sample images (e.g., including thered image and the yellow image), and can estimate the flow cell tiltangle with the red image of the first frame of the blood sample image.The system can then perform the tilt correction on the one or moreframes of sample images and one or more frames of blood sample imagesusing the flow cell tilt angle estimated with the red image of the firstframe of the blood sample images.

In some implementations, the operations of the process 900 canoptionally include estimating an image focus of the tilt corrected image(906). For example, the system can estimate an image focus score inaccordance with the techniques described in connection with FIG. 7 . Forexample, the system can estimate an image focus score on a yellow bloodsample image. The system can determine whether the image focus score islower than a predetermined threshold, and if so, discard the image andobtain another image.

Operations of the process 900 also include performing edge detection onthe tilt corrected image (908). Operations of the process 900 alsoinclude selecting an initial plasma ROI based on the detected edges(910). For example, the system can perform the edge detection and ROIselection in accordance with the techniques described in connection withFIGS. 3A-3B. Similar to the tilt correction in step 904, in someimplementations, the system can import a single frame (e.g., the firstframe) of a sequence of images and perform the edge detection based onthe single frame of the sequence of images. For example, the system canimport the first frame of the blood sample images (e.g., including thered image and the yellow image), and can detect the flow cell edgesusing the yellow image of the first frame of the blood sample image.Then the system can determine the initial plasma ROI based on thedetected flow cell edges.

In some implementations, the operations of the process 900 canoptionally include rendering the initial plasma ROI on a display orsaving the initial plasma ROI as an image (912). For example, the systemcan render the initial plasma ROI on a display such that a user canreview the ROI, determine the quality of the ROI, or perform analysis ofan analyte in the ROI. As another example, the system can save theinitial plasma ROI as an image (or a video if the input to the system ismultiple frames of images) in the memory or hard drive of a localcomputer or a remote server, such that the image can be retrieved andanalyzed (either manually or using a software) at a later time.

Operations of the process 900 can also include detecting image artifacts(e.g., an air bubble) in the initial plasma ROI (914). If the systemdetermines that an image artifact is in the initial plasma ROI, theoperations of the process 900 can include generating an updated plasmaROI by removing the image artifact (916). If the system determines thatthe initial plasma ROI does not include an image artifact, the systemcan execute step 918 directly. For example, the system can perform theair bubble detection and removal in accordance with the techniquesdescribed in connection with FIGS. 5A-5D. In some implementations, thesystem can calculate a non-rectangularity and/or perform blob analysis.Because the plasma ROI typically has a rectangular shape, a wavyappearance or a blob shape can indicate the presence of air bubbles orother fluidic and plasma separation issues. In some implementations, thesystem can first generate an OD image (e.g., a red OD image and/or ayellow OD image) and can calculate % CV (i.e., the coefficient ofvariation) within the OD image (e.g., % CV within the yellow plasma ODimage). The system can detect one or more air bubbles using apredetermined rule. For example, if (i) the non-rectangularity is largerthan 0.45, (ii) the number of blobs is larger than one, and (iii) the %CV in the yellow OD is larger than 45, the system can determine theimage has at least one air bubble. If a suspected air bubble isdetected, the system can redraw the plasma ROI by excluding thesuspected air bubble (e.g., one or more round blobs). In someimplementations, if the system has already estimated a hemoglobin valuein the initial plasma ROI, the system can repeat the hemoglobin valueestimate process for the updated ROI and generate an updated hemoglobinvalue.

Operations of the process 900 can also include detecting interferents(e.g., high lipid, clots) in the plasma ROI (918). If the systemdetermines that interferents are in the plasma ROI (e.g., the initialROI, or the updated ROI after removing the air bubble), the operationsof the process 900 can include generating an updated plasma ROI (e.g.,by including or removing the interferences) (920). If the systemdetermines that the plasma ROI does not include interferents, the systemcan go directed to step 922 of the process 900. For example, the systemcan perform the high lipid detection in accordance with the techniquesdescribed in connection with FIGS. 4A-4C. After determining high lipidexists in the initial plasma ROI, the system can include the high lipidby redrawing the plasma ROI. For example, the system can first use abounding box to include the initial ROI and the system can reduce theheight of the bounding box to a predetermined percentage of the originalheight (e.g., 75% of the original height). The region of the boundingbox at the reduced height is the updated plasma ROI that includes thehigh lipids. In some implementations, if the system has alreadyestimated a hemoglobin value in the initial plasma ROI or the updatedplasma ROI after removing one or more air bubbles, the system can repeatthe hemoglobin value estimate process for the updated plasma ROI thatincludes the high lipid and generate an updated hemoglobin value.

Operations of the process 900 can also include estimating hemoglobinvalue in the plasma ROI (922). The system can estimate the hemoglobinvalue using an OD algorithm or a concentration algorithm. For example,with the OD algorithm, the system can calculate a red plasma OD imageand a yellow plasma OD image. The system can calculate a peak value inthe yellow OD image and a peak value in the red OD image by histogramfitting. The system can apply the extinction coefficients to the peakvalue to estimate the hemoglobin value. For example, with theconcentration algorithm, the system can calculate a red plasma OD imageand a yellow plasma OD image. The system can estimate the hemoglobinvalue using the concentration algorithm. For example, in theconcentration algorithm, extinction coefficients can be applied beforethe histogram fitting step.

In some implementations, the operations of the process 900 can alsoinclude a glass correction (i.e., intensity normalization) process. Whencalculating the OD images, e.g., with the OD algorithm or theconcentration algorithm, the system can perform a glass correctionprocess in accordance with the techniques described in connection withFIGS. 8A-8B. For example, the system can select the glass area (i.e.,the glass ROI) in a red blood sample image based on the location of theflow cell edges detected in step 908. The system can calculate theaverage intensity in the glass ROI for both the reference image and theblood sample image. The system can also calculate the average intensityin the plasma ROI for the reference image and the sample image. Thesystem can perform the glass correction based on the average intensitiesin the glass ROI and the plasma ROI, e.g., following equation (2).

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be for a special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural,object-oriented, assembly, and/or machine language. As used herein, theterms machine-readable medium and computer-readable medium refer to anycomputer program product, apparatus and/or device (e.g., magnetic discs,optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term machine-readable signal refers toany signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a GUI or a web browser through which a user can interact with animplementation of the systems and techniques described here), or anycombination of such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, such as network 210 of FIG. 2 . Examples ofcommunication networks include a LAN, a WAN, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications may be made without departing from the scope of theinventive concepts described herein, and, accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A computer-implemented method comprising:obtaining an image of a fluid of a microfluidic analysis system, whereinthe microfluidic analysis system comprises or receives a container thatcontains the fluid for measurement of analyte or quality determination,and the image is captured using an imaging device associated with themicrofluidic analysis system; identifying, based on the image, a regionof interest (ROI), wherein the ROI is a set of pixel values for use inthe measurement of the analyte or the quality determination of thefluid, fluidic path, or measuring system and wherein identifying the ROIcomprises determining an alignment of the container of the fluid withthe imaging device based on the image, and identifying the ROI based oninformation about the measurement of the fluid or based on informationabout non-analyte features of the fluid; and performing an analysis ofthe image of the fluid using the set of pixel values of the ROI.
 2. Themethod of claim 1, wherein the fluid is a whole blood sample, and theimage represents the whole blood sample with blood plasma separated fromred blood cells.
 3. The method of claim 2, wherein identifying the ROIcomprises identifying, in the image, a portion representing the bloodplasma, wherein identifying the portion representing the blood plasmacomprises: detecting a plurality of reference features associated withthe container of the fluid; identifying, based on the referencefeatures, a candidate region for the ROI; and performingclustering-based thresholding of pixel values within the candidateregion to identify the portion representing the blood plasma or usingone or more neural networks for identifying the ROI.
 4. The method ofclaim 1, wherein the measurement of the analyte comprises a parameterindicative of hemolysis (hemoglobin) in a portion representing bloodplasma.
 5. The method of claim 1, wherein the measurement of the analytecomprises a parameter indicative of lipemia (or lipids) in a portionrepresenting blood plasma.
 6. The method of claim 1, wherein themeasurement of the analyte comprises a parameter indicative of Icterus(or bilirubin) in a portion representing blood plasma.
 7. The method ofclaim 1, further comprising: determining that the ROI excludes a portionthat represents lipid in blood plasma; and identifying an updated ROIsuch that the updated ROI comprises a bounding box that includes theportion that represents the lipid.
 8. The method of claim 1, wherein thequality determination of the fluid, the fluidic path, or the measuringsystem comprises: determining quality of an assay, determining qualityof a sample, and determining integrity of the fluidic path or themeasuring system impacting the ROI.
 9. The method of claim 1, whereinthe quality determination of the fluid, the fluidic path, or themeasuring system comprises: determining that the ROI includes a portionthat represents an air bubble in the fluid; and identifying an updatedROI such that the updated ROI excludes the portion that represents theair bubble.
 10. The method of claim 1, further comprising: detecting anamount of tilt in the image of the fluid, the tilt resulting from thealignment of the container of the fluid with the imaging device; andgenerating, based on the amount of the tilt, a rotation-corrected imageof the fluid, wherein the ROI is identified in the rotation-correctedimage.
 11. The method of claim 1, wherein performing the analysis of theimage of the fluid comprises: generating an image focus score associatedwith the image; determining that the image focus score is lower than apredetermined threshold; and discarding the image of the fluidresponsive to determining that the image focus score is lower than thepredetermined threshold.
 12. The method of claim 1, wherein performingthe analysis of the image of the fluid comprises: identifying, in theimage, a portion representing a transparent portion of the container;and using brightness of the portion representing the transparent portionof the container as a reference point to evaluate brightness of otherportions of the image.
 13. The method of claim 1, further comprising:monitoring one or more optical characteristics of the fluid atpredetermined intervals.
 14. The method of claim 1, further comprising:identifying a target optical interference pattern in the image; andgenerating an alert in response to identifying the target opticalinterference pattern in the image.
 15. A system comprising: amicrofluidic analysis apparatus that comprises or receives a containerconfigured to hold a fluid for measurement of analyte or qualitydetermination; an imaging device configured to obtain an image of thefluid in the container; and one or more processing devices configuredto: identify, based on the image, a region of interest (ROI), whereinthe ROI is a set of pixel values for use in the measurement of theanalyte or the quality determination of the fluid, fluidic path, or themicrofluidic analysis apparatus, and wherein identifying the ROIcomprises: determining an alignment of the container of the fluid withthe imaging device based on the image, and identifying the ROI based oninformation about the measurement of the fluid or based on informationabout non-analyte features of the fluid; and perform an analysis of theimage of the fluid using the set of pixel values of the ROI.
 16. Thesystem of claim 15, wherein the fluid is a whole blood sample, and theimage represents the whole blood sample with blood plasma separated fromred blood cells.
 17. The system of claim 16, wherein the blood plasma isseparated from the red blood cells within the microfluidic analysisapparatus using 2-150 uL of the whole blood sample.
 18. The system ofclaim 15, wherein the one or more processing devices are configured to:identify a target optical interference pattern in the image; andgenerate an alert in response to identifying the target opticalinterference pattern in the image.
 19. A non-transitory,computer-readable medium storing one or more instructions executable bya computer system to perform operations comprising: obtaining an imageof a fluid of a microfluidic analysis system, wherein the microfluidicanalysis system comprises or receives a container that contains thefluid for measurement of analyte or quality determination, and the imageis captured using an imaging device associated with the microfluidicanalysis system; identifying, based on the image, a region of interest(ROI), wherein the ROI is a set of pixel values for use in themeasurement of analyte or the quality determination of the fluid,fluidic path, or measuring system and wherein identifying the ROIcomprises determining an alignment of the container of the fluid withthe imaging device based on the image, and identifying the ROI based oninformation about the measurement of the fluid or based on informationabout non-analyte features of the fluid; and performing an analysis ofthe image of the fluid using the set of pixel values of the ROI.
 20. Thenon-transitory, computer-readable medium of claim 19, whereinidentifying the ROI comprises identifying, in the image, a portionrepresenting blood plasma, wherein identifying the portion representingthe blood plasma comprises: detecting a plurality of reference featuresassociated with the container of the fluid; identifying, based on thereference features, a candidate region for the ROI; and performingclustering-based thresholding of pixel values within the candidateregion to identify the portion representing the blood plasma.